




'''simple docstring'''

import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			GPTaTokenizer
			__SCREAMING_SNAKE_CASE    =			GPTaTokenizerFast
			__SCREAMING_SNAKE_CASE    =			True
			__SCREAMING_SNAKE_CASE    =			{"""add_prefix_space""": True}
			__SCREAMING_SNAKE_CASE    =			False





			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__snake_case			=		[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						    "<|endoftext|>",
						]
						__snake_case			=		dict(zip(a_      ,		range(len(a_							)							)							)							)
						__snake_case			=		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						__snake_case			=		{"unk_token": "<unk>"}

						__snake_case			=		os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["vocab_file"]							)
						__snake_case			=		os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["merges_file"]							)
						with open(self.vocab_file      ,		"w"      ,		encoding="utf-8"							) as fp:
									fp.write(json.dumps(a_							) + "\n"							)
						with open(self.merges_file      ,		"w"      ,		encoding="utf-8"							) as fp:
									fp.write("\n".join(a_							)							)





			def 							A (      self						:    List[str]      ,		**a_						:    Union[str, Any]							):



						"""simple docstring"""




						kwargs.update(self.special_tokens_map							)
						return GPTaTokenizer.from_pretrained(self.tmpdirname      ,		**a_							)





			def 							A (      self						:    Union[str, Any]      ,		**a_						:    Tuple							):



						"""simple docstring"""




						kwargs.update(self.special_tokens_map							)
						return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,		**a_							)





			def 							A (      self						:    List[str]      ,		a_						:    Any							):



						"""simple docstring"""




						__snake_case			=		"lower newer"
						__snake_case			=		"lower newer"
						return input_text, output_text





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		GPTaTokenizer(self.vocab_file      ,		self.merges_file      ,		**self.special_tokens_map							)
						__snake_case			=		"lower newer"
						__snake_case			=		["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
						__snake_case			=		tokenizer.tokenize(a_      ,		add_prefix_space=a_							)
						self.assertListEqual(a_      ,		a_							)

						__snake_case			=		tokens + [tokenizer.unk_token]
						__snake_case			=		[14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(a_							)      ,		a_							)





			def 							A (      self						:    Any							):



						"""simple docstring"""




						if not self.test_rust_tokenizer:
									return

						__snake_case			=		self.get_tokenizer()
						__snake_case			=		self.get_rust_tokenizer(add_prefix_space=a_							)

						__snake_case			=		"lower newer"

						# Testing tokenization
						__snake_case			=		tokenizer.tokenize(a_      ,		add_prefix_space=a_							)
						__snake_case			=		rust_tokenizer.tokenize(a_							)
						self.assertListEqual(a_      ,		a_							)

						# Testing conversion to ids without special tokens
						__snake_case			=		tokenizer.encode(a_      ,		add_special_tokens=a_      ,		add_prefix_space=a_							)
						__snake_case			=		rust_tokenizer.encode(a_      ,		add_special_tokens=a_							)
						self.assertListEqual(a_      ,		a_							)

						# Testing conversion to ids with special tokens
						__snake_case			=		self.get_rust_tokenizer(add_prefix_space=a_							)
						__snake_case			=		tokenizer.encode(a_      ,		add_prefix_space=a_							)
						__snake_case			=		rust_tokenizer.encode(a_							)
						self.assertListEqual(a_      ,		a_							)

						# Testing the unknown token
						__snake_case			=		tokens + [rust_tokenizer.unk_token]
						__snake_case			=		[14, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a_							)      ,		a_							)





			def 							A (      self						:    Any      ,		*a_						:    int      ,		**a_						:    Optional[Any]							):



						"""simple docstring"""




						pass





			def 							A (      self						:    Union[str, Any]      ,		a_						:    Tuple=15							):



						"""simple docstring"""




						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
												__snake_case			=		self.rust_tokenizer_class.from_pretrained(a_      ,		**a_							)

												# Simple input
												__snake_case			=		"This is a simple input"
												__snake_case			=		["This is a simple input 1", "This is a simple input 2"]
												__snake_case			=		("This is a simple input", "This is a pair")
												__snake_case			=		[
												    ("This is a simple input 1", "This is a simple input 2"),
												    ("This is a simple pair 1", "This is a simple pair 2"),
												]

												# Simple input tests
												self.assertRaises(a_      ,		tokenizer_r.encode      ,		a_      ,		max_length=a_      ,		padding="max_length"							)

												# Simple input
												self.assertRaises(a_      ,		tokenizer_r.encode_plus      ,		a_      ,		max_length=a_      ,		padding="max_length"							)

												# Simple input
												self.assertRaises(
												    a_      ,		tokenizer_r.batch_encode_plus      ,		a_      ,		max_length=a_      ,		padding="max_length"      ,		)

												# Pair input
												self.assertRaises(a_      ,		tokenizer_r.encode      ,		a_      ,		max_length=a_      ,		padding="max_length"							)

												# Pair input
												self.assertRaises(a_      ,		tokenizer_r.encode_plus      ,		a_      ,		max_length=a_      ,		padding="max_length"							)

												# Pair input
												self.assertRaises(
												    a_      ,		tokenizer_r.batch_encode_plus      ,		a_      ,		max_length=a_      ,		padding="max_length"      ,		)





			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		GPTaTokenizer.from_pretrained(self.tmpdirname      ,		pad_token="<pad>"							)

						# Simple input
						__snake_case			=		"This is a simple input"
						__snake_case			=		["This is a simple input looooooooong", "This is a simple input"]
						__snake_case			=		("This is a simple input", "This is a pair")
						__snake_case			=		[
						    ("This is a simple input loooooong", "This is a simple input"),
						    ("This is a simple pair loooooong", "This is a simple pair"),
						]

						__snake_case			=		tokenizer.pad_token_id

						__snake_case			=		tokenizer(a_      ,		padding="max_length"      ,		max_length=30      ,		return_tensors="np"							)
						__snake_case			=		tokenizer(a_      ,		padding=a_      ,		truncate=a_      ,		return_tensors="np"							)
						__snake_case			=		tokenizer(*a_      ,		padding="max_length"      ,		max_length=60      ,		return_tensors="np"							)
						__snake_case			=		tokenizer(a_      ,		padding=a_      ,		truncate=a_      ,		return_tensors="np"							)

						# s
						# test single string max_length padding
						self.assertEqual(out_s["input_ids"].shape[-1]      ,		30							)
						self.assertTrue(pad_token_id in out_s["input_ids"]							)
						self.assertTrue(0 in out_s["attention_mask"]							)

						# s2
						# test automatic padding
						self.assertEqual(out_sa["input_ids"].shape[-1]      ,		33							)
						# long slice doesn't have padding
						self.assertFalse(pad_token_id in out_sa["input_ids"][0]							)
						self.assertFalse(0 in out_sa["attention_mask"][0]							)
						# short slice does have padding
						self.assertTrue(pad_token_id in out_sa["input_ids"][1]							)
						self.assertTrue(0 in out_sa["attention_mask"][1]							)

						# p
						# test single pair max_length padding
						self.assertEqual(out_p["input_ids"].shape[-1]      ,		60							)
						self.assertTrue(pad_token_id in out_p["input_ids"]							)
						self.assertTrue(0 in out_p["attention_mask"]							)

						# p2
						# test automatic padding pair
						self.assertEqual(out_pa["input_ids"].shape[-1]      ,		52							)
						# long slice pair doesn't have padding
						self.assertFalse(pad_token_id in out_pa["input_ids"][0]							)
						self.assertFalse(0 in out_pa["attention_mask"][0]							)
						# short slice pair does have padding
						self.assertTrue(pad_token_id in out_pa["input_ids"][1]							)
						self.assertTrue(0 in out_pa["attention_mask"][1]							)





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		"$$$"
						__snake_case			=		GPTaTokenizer.from_pretrained(self.tmpdirname      ,		bos_token=a_      ,		add_bos_token=a_							)

						__snake_case			=		"This is a simple input"
						__snake_case			=		["This is a simple input 1", "This is a simple input 2"]

						__snake_case			=		tokenizer.bos_token_id

						__snake_case			=		tokenizer(a_							)
						__snake_case			=		tokenizer(a_							)

						self.assertEqual(out_s.input_ids[0]      ,		a_							)
						self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids							)							)

						__snake_case			=		tokenizer.decode(out_s.input_ids							)
						__snake_case			=		tokenizer.batch_decode(out_sa.input_ids							)

						self.assertEqual(decode_s.split()[0]      ,		a_							)
						self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa							)							)





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						pass






			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		[self.get_tokenizer(do_lower_case=a_      ,		add_bos_token=a_							)]
						for tokenizer in tokenizers:
									with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
												__snake_case			=		"Encode this."
												__snake_case			=		"This one too please."
												__snake_case			=		tokenizer.encode(a_      ,		add_special_tokens=a_							)
												encoded_sequence += tokenizer.encode(a_      ,		add_special_tokens=a_							)
												__snake_case			=		tokenizer.encode_plus(
												    a_      ,		a_      ,		add_special_tokens=a_      ,		return_special_tokens_mask=a_      ,		)
												__snake_case			=		encoded_sequence_dict["input_ids"]
												__snake_case			=		encoded_sequence_dict["special_tokens_mask"]
												self.assertEqual(len(a_							)      ,		len(a_							)							)

												__snake_case			=		[
												    (x if not special_tokens_mask[i] else None) for i, x in enumerate(a_							)
												]
												__snake_case			=		[x for x in filtered_sequence if x is not None]
												self.assertEqual(a_      ,		a_							)







@require_tokenizers
class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		AutoTokenizer.from_pretrained("facebook/opt-350m"      ,		from_slow=a_							)
						__snake_case			=		"A photo of a cat"

						__snake_case			=		tokenizer.encode(
						    a_      ,		)
						self.assertEqual(a_      ,		[2, 250, 1_345, 9, 10, 4_758]							)
						tokenizer.save_pretrained("test_opt"							)

						__snake_case			=		AutoTokenizer.from_pretrained("./test_opt"							)
						__snake_case			=		tokenizer.encode(
						    a_      ,		)
						self.assertEqual(a_      ,		[2, 250, 1_345, 9, 10, 4_758]							)





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		AutoTokenizer.from_pretrained("facebook/opt-350m"      ,		use_slow=a_							)
						__snake_case			=		"A photo of a cat"

						__snake_case			=		tokenizer.encode(
						    a_      ,		)
						# Same as above
						self.assertEqual(a_      ,		[2, 250, 1_345, 9, 10, 4_758]							)






			@unittest.skip("This test is failing because of a bug in the fast tokenizer"							)
			def 							A (      self						:    List[str]							):



						"""simple docstring"""




						__snake_case			=		AutoTokenizer.from_pretrained("facebook/opt-350m"      ,		from_slow=a_							)

						__snake_case			=		"bos"
						__snake_case			=		tokenizer.get_vocab()["bos"]

						__snake_case			=		"A photo of a cat"
						__snake_case			=		tokenizer.encode(
						    a_      ,		)
						# We changed the bos token
						self.assertEqual(a_      ,		[31_957, 250, 1_345, 9, 10, 4_758]							)
						tokenizer.save_pretrained("./tok"							)
						__snake_case			=		AutoTokenizer.from_pretrained("./tok"							)
						self.assertTrue(tokenizer.is_fast							)
						__snake_case			=		tokenizer.encode(
						    a_      ,		)
						self.assertEqual(a_      ,		[31_957, 250, 1_345, 9, 10, 4_758]							)









import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()

         snake_case											=							[
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''的''',
             '''价''',
             '''格''',
             '''是''',
             '''15''',
             '''便''',
             '''alex''',
             '''##andra''',
             '''，''',
             '''。''',
             '''-''',
             '''t''',
             '''shirt''',
         ]
         snake_case											=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES['''vocab_file''']       )
         with open(self.vocab_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as vocab_writer:
              vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

         snake_case											=							{
             '''do_resize''': True,
             '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
             '''do_center_crop''': True,
             '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
             '''do_normalize''': True,
             '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
             '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
             '''do_convert_rgb''': True,
         }
         snake_case											=							os.path.join(self.tmpdirname     ,   __snake_case       )
         with open(self.image_processor_file     ,   '''w'''     ,   encoding='''utf-8'''       ) as fp:
              json.dump(__snake_case     ,   __snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizer.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							[np.random.randint(2_5_5     ,   size=(3, 3_0, 4_0_0)     ,   dtype=np.uinta       )]

         snake_case											=							[Image.fromarray(np.moveaxis(__snake_case     ,   0     ,   -1       )       ) for x in image_inputs]

         return image_inputs
    def 						a_     (			self       ):
         snake_case											=							self.get_tokenizer()
         snake_case											=							self.get_rust_tokenizer()
         snake_case											=							self.get_image_processor()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_slow.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,   use_fast=__snake_case       )

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )
         processor_fast.save_pretrained(self.tmpdirname       )
         snake_case											=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor_slow.tokenizer.get_vocab()     ,   tokenizer_slow.get_vocab()       )
         self.assertEqual(processor_fast.tokenizer.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertEqual(tokenizer_slow.get_vocab()     ,   tokenizer_fast.get_vocab()       )
         self.assertIsInstance(processor_slow.tokenizer     ,   __snake_case       )
         self.assertIsInstance(processor_fast.tokenizer     ,   __snake_case       )

         self.assertEqual(processor_slow.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertEqual(processor_fast.image_processor.to_json_string()     ,   image_processor.to_json_string()       )
         self.assertIsInstance(processor_slow.image_processor     ,   __snake_case       )
         self.assertIsInstance(processor_fast.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         snake_case											=							self.get_tokenizer(cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''       )
         snake_case											=							self.get_image_processor(do_normalize=__snake_case       )

         snake_case											=							ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname     ,   cls_token='''(CLS)'''     ,   sep_token='''(SEP)'''     ,   do_normalize=__snake_case       )

         self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.tokenizer     ,   __snake_case       )

         self.assertEqual(processor.image_processor.to_json_string()     ,   image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							self.prepare_image_inputs()

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''

         snake_case											=							processor(text=__snake_case       )

         snake_case											=							tokenizer(__snake_case       )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         snake_case											=							processor.batch_decode(__snake_case       )
         snake_case											=							tokenizer.batch_decode(__snake_case       )

         self.assertListEqual(__snake_case     ,   __snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_tokenizer()

         snake_case											=							ChineseCLIPProcessor(tokenizer=__snake_case     ,   image_processor=__snake_case       )

         snake_case											=							'''Alexandra，T-shirt的价格是15便士。'''
         snake_case											=							self.prepare_image_inputs()

         snake_case											=							processor(text=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   processor.model_input_names       )




